The motion of the carrier is determined by means of a navigation system comprising one or more navigation sensors carried aboard said carrier. The navigation system determines the motion by an adapted processing of the measurements provided by the navigation sensors. The navigation sensors may be of different types, such as a GPS receiver, accelerometer, odometer, gyroscope, Doppler radar, etc.
The estimating of the motion of the carrier with respect to the environment is done by implementing a navigation filter combining a model of displacement (for example, the kinematic equations of the carrier) with the navigation measurements provided by the navigation sensors.
However, there are many scenarios in which the navigation system alone is not able to correctly estimate the motion of the carrier with respect to the environment.
This is the case, for example, with a wheeled carrier moving on the surface of a ground and provided with an odometer measuring the rotation of the wheels. The odometer measurements are not enough to reconstitute the motion of the carrier when it is slipping or sliding on the ground.
This is also the case with a carrier moving in relation to a celestial body and having an inertial navigation unit providing measurements of angular velocities and linear accelerations along at least three independent axes. A navigation filter processes these measurements at a high rate (typically 150 Hertz) by using a local model of the gravitational field, for example through an extended Kalman filter, in order to return the position and velocity of the carrier with respect to the celestial body. The inevitable deviations of the inertial navigation sensors and the initial lack of knowledge of the position of the carrier with respect to the environment are so many sources of error in the estimation of the motion of said carrier, especially the estimation of its position with respect to the environment. These errors build up and propagate over time in the following estimations.
This is also the case with a carrier having a GPS receiver. Even though such a GPS receiver is generally adapted to provide a precise estimation of the position of the carrier (typically within a meter in certain cases), this precision is degraded in the event of loss or corruption of the GPS measurements. Such a loss or corruption of the GPS measurements may occur in particular due to obstacles in a radio channel between the carrier and the GPS transmitter and/or due to multipath phenomena for said radio channel.
In order to remedy these limitations, it is known to implement a vision system and provide the carrier with one or more vision sensors, which perform the acquisition of two-dimensional images (2D images) of the environment.
The information coming from the vision system is processed by the navigation system. For this, the navigation filter is augmented to take into account the measurements provided by the vision system.
Numerous digital implementations of the augmented navigation filter may be produced, for example, a Kalman filter, an extended Kalman filter, an information filter, a particle filter, a Bayesian filter, etc.
Examples of such augmented navigation systems are described, for example, in the context of spacecraft-type carriers, in the following scientific publications:                “Navigation for Planetary Approach and Landing”, B. Frapard et al., 5th International ESA Conference on Guidance Navigation and Control Systems, 22-25 Oct. 2002, Frascati, Italy;        “Autonomous Navigation Concepts for Interplanetary Missions”, B. Polle et al., IFAC Symposium on Automatic Control in Aerospace 14-18 Jun. 2004, Saint Petersburg, Russia;        “Mars Sample Return: Optimising the Descent and Soft Landing for an Autonomous Martian Lander”, X. Sembély et al., Symposium on Atmospheric Reentry Vehicles and Systems, 23 Mar. 2005, Arcachon, France;        “Vision Navigation for European Landers and the NPAL Project”, G. Bodineau et al., IFAC Symposium on Automatic Control in Aerospace, 25-29 Jun. 2007, Toulouse, France.        
In these scientific publications, an inertial navigation system is considered which is based on navigation measurements among which measurements of linear accelerations of the vehicle along the three axes of a reference frame tied to the vehicle, and measurements of angular velocities of the vehicle along these three axes. The vector of states of the inertial navigation filter comprises states related to the motion such as the position, velocity, and angles of attitude of the vehicle in a reference frame tied to the environment. The navigation filter propagates (prediction step) an estimation of the vector of states taking into account a local model of the gravitational field, as well as the covariance of the errors in the estimation of these states. The navigation filter readjusts (updating step) the estimation of the vector of states, and consequently that of the motion of the vehicle, based on the navigation measurements.
In these scientific publications, the inertial navigation system is combined with a vision system consisting in a camera carried aboard the vehicle and providing 2D images of the environment at a typical frequency on the order of 10 to 100 Hertz.
In an image, characteristic zones of the environment are identified. A characteristic zone of the environment is a zone whose representation in the image, as an assemblage of pixels, has the property of being able to be found from one image to another, for example, by image correlation or by shape recognition. A characteristic zone of the image may be, for example, a patch of several pixels to several tens of pixels in which there exist variations of luminance or texture or contrast in at least two directions.
One tracks the characteristic zones of the environment from one image to another by image correlation. Each characteristic zone is associated with one point in the image, known as a “characteristic point of the image” Mi. The characteristic point Mi is, for example, the radiometric or geometric center of gravity of the patch of pixels representing this characteristic zone, or a particular point of this characteristic zone. The displacement of the characteristic points Mi from one image to another is representative of the motion in translation and rotation of the vehicle with respect to the environment.
The motion of the vehicle in position and attitude is estimated by augmenting the vector of states of the inertial navigation filter with the coordinates, in a reference frame, of characteristic points M′i of the environment represented by the characteristic points Mi of the image. A reference frame tied to the carrier is denoted by (O, X, Y, Z), and is defined for example by taking O as the center of the focal plane of the camera, Z as the optical axis of the camera, and (X, Y) as the focal plane of the camera.
To estimate the vector of states of the navigation filter, besides the measurements of the inertial navigation system, one uses 2M measurements corresponding to the directions of the vectors OM′i in the reference frame.
The navigation filter being so augmented both in regard to the states and in regard to the measurements, one estimates the vector of states of the navigation filter as a function of a model of the variation over time of the states of the system and a model of the different measurements.
However, the principal drawback of the navigation systems also utilizing measurements obtained from images lies in the fact that the estimation of the state of the carrier, by means of a navigation filter so augmented with states and measurements associated with the characteristic points M′i, requires major computing power.
It is known from patent FR 2964774 B1 to calculate, from the coordinates of the characteristic points identified in a triplet of images, at least one condensed measurement representative of the motion of the carrier during the acquisition of said triplet of images. The navigation filter then performs a merging of the navigation measurements and the at least one condensed measurement, instead of a merging of the navigation measurements and the coordinates of all the characteristic points. The number of necessary calculations is thus greatly reduced, while improving the performance of the estimation of the motion of the carrier, which likewise benefits from information derivable from images acquired by the vision sensor.
However, the solution described in patent FR 2964774 B1 encounters a problem of ambiguity which is inherent to 2D vision. In fact, when the scene observed is substantially planar, there exist several possible interpretations of the motion leading to the same observation for certain trajectories of the carrier, especially substantially orthogonal to said planar scene, and even a degeneration of the solution not allowing a calculation of a condensed measurement. This problem of ambiguity occurs most particularly in the case of a scenario of landing of the carrier on a celestial body.